SoroushVahidi/scidocs huggingface dataset:
Norepinephrine transporter occupancy by antidepressant in human brain using positron emission tomography with (S,S)-[18F]FMeNER-D2
(2010)

Central norepinephrine transporter (NET) is one of the main targets of antidepressants. Although the measurement of NET occupancy has been attempted in humans, the outcomes have been inconclusive. In this study, the occupancy of NET by different doses of an antidepressant, nortriptyline, was measured using positron emission tomography (PET) with (S,S)-[18F]FMeNER-D2. PET scans using (S,S)-[18F]FMeNER-D2 were performed on six healthy men before and after oral administration of a single oral dose of nortriptyline (10–75 mg). After a bolus i.v. injection of (S,S)-[18F]FMeNER-D2, dynamic scanning was performed for 0–90 min, followed by scanning for 120–180 min. The ratio of the thalamus-to-caudate areas under the curve (120–180 min) minus 1 was used as the binding potential (BPND) for NET. NET occupancy was calculated as the percentage reduction of BPND. Venous blood samples were taken to measure the concentrations of nortriptyline just before injection of the tracer and at 180 min after the injection. Mean NET occupancies by nortriptyline were 16.4% at 10 mg, 33.2% at 25 mg, and 41.1% at 75 mg. The mean plasma concentration of nortriptyline was less than the lower limit of detection at 10 mg, 23.7 ng/mL at 25 mg, and 50.5 ng/mL at 75 mg. Estimated ED50 was 76.8 mg of administration dose and 59.8 ng/mL of plasma concentration. NET occupancy by nortriptyline corresponding to the administration dose of 10–75 mg or plasma concentration was observed from 16% to 41%.
A neuromorphic architecture for anomaly detection in autonomous large-area traffic monitoring
(2013)

The advanced sensing and imaging capability of today's sensor networks enables real time monitoring in a large area. In order to provide continuous monitoring and prompt situational awareness, an abstract-level autonomous information processing framework is developed that is able to detect various categories of abnormal traffic events with unsupervised learning. The framework is based on cogent confabulation model, which performs statistical inference in a manner inspired by human neocortex system. It enables detection and recognition of abnormal target vehicles within the context of surrounding traffic activities and previous events using likelihood-ratio test. A neuromorphic architecture is proposed which accelerates the computation for real-time detection by leveraging memristor crossbar arrays.
Ontogenesis of Agency in Machines : A Multidisciplinary Review
(2014)

How does agency arise in biocognitive systems? Can the emergence of agency be explained by physical laws? We review these and related questions in philosophy, psychology, biology, and physics. Based on the review we ask the questions: i) Can machines have agency? and, if so, ii) How can we build machines with agency? We examine existing work in artificial intelligence and machine learning on selfmotivated, self-teaching, and self-developing systems with respect to the ontogenesis (“coming into being”) of agency in computational systems. The impact of these “autogenic” systems or machine agency on science, technology, and humanity will be discussed.
CODRA: A Novel Discriminative Framework for Rhetorical Analysis
(2015)

Clauses and sentences rarely stand on their own in an actual discourse; rather, the relationship between them carries important information that allows the discourse to express a meaning as a whole beyond the sum of its individual parts. Rhetorical analysis seeks to uncover this coherence structure. In this article, we present CODRA— a COmplete probabilistic Discriminative framework for performing Rhetorical Analysis in accordance with Rhetorical Structure Theory, which posits a tree representation of a discourse.CODRA comprises a discourse segmenter and a discourse parser. First, the discourse segmenter, which is based on a binary classifier, identifies the elementary discourse units in a given text. Then the discourse parser builds a discourse tree by applying an optimal parsing algorithm to probabilities inferred from two Conditional Random Fields: one for intra-sentential parsing and the other for multi-sentential parsing. We present two approaches to combine these two stages of parsing effectively. By conducting a series of empirical evaluations over two different data sets, we demonstrate that CODRA significantly outperforms the state-of-the-art, often by a wide margin. We also show that a reranking of the k-best parse hypotheses generated by CODRA can potentially improve the accuracy even further.
An Access Control scheme for Big Data processing
(2014)

Access Control (AC) systems are among the most critical of network security components. A system's privacy and security controls are more likely to be compromised due to the misconfiguration of access control policies rather than the failure of cryptographic primitives or protocols. This problem becomes increasingly severe as software systems become more and more complex, such as Big Data (BD) processing systems, which are deployed to manage a large amount of sensitive information and resources organized into a sophisticated BD processing cluster. Basically, BD access control requires the collaboration among cooperating processing domains to be protected as computing environments that consist of computing units under distributed AC managements. Many BD architecture designs were proposed to address BD challenges; however, most of them were focused on the processing capabilities of the “three Vs” (Velocity, Volume, and Variety). Considerations for security in protecting BD are mostly ad hoc and patch efforts. Even with some inclusion of security in recent BD systems, a critical security component, AC (Authorization), for protecting BD processing components and their users from the insider attacks, remains elusive. This paper proposes a general purpose AC scheme for distributed BD processing clusters.
Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement
(2007)

Histogram equalization (HE) is one of the common methods used for improving contrast in digital images. However, this technique is not very well suited to be implemented in consumer electronics, such as television, because the method tends to introduce unnecessary visual deterioration such as the saturation effect. One of the solutions to overcome this weakness is by preserving the mean brightness of the input image inside the output image. This paper proposes a new method, known as brightness preserving dynamic histogram equalization (BPDHE), which is an extension to HE that can produce the output image with the mean intensity almost equal to the mean intensity of the input, thus fulfill the requirement of maintaining the mean brightness of the image. First, the method smoothes the input histogram with one dimensional Gaussian filter, and then partitions the smoothed histogram based on its local maximums. Next, each partition will be assigned to a new dynamic range. After that, the histogram equalization process is applied independently to these partitions, based on this new dynamic range. For sure, the changes in dynamic range, and also histogram equalization process will alter the mean brightness of the image. Therefore, the last step in this method is to normalize the output image to the input mean brightness. Our results from 80 test images shows that this method outperforms other present mean brightness preserving histogram equalization methods. In most cases, BPDHE successfully enhance the image without severe side effects, and at the same time, maintain the mean input brightness1.
Language, music, syntax and the brain
(2003)

The comparative study of music and language is drawing an increasing amount of research interest. Like language, music is a human universal involving perceptually discrete elements organized into hierarchically structured sequences. Music and language can thus serve as foils for each other in the study of brain mechanisms underlying complex sound processing, and comparative research can provide novel insights into the functional and neural architecture of both domains. This review focuses on syntax, using recent neuroimaging data and cognitive theory to propose a specific point of convergence between syntactic processing in language and music. This leads to testable predictions, including the prediction that that syntactic comprehension problems in Broca's aphasia are not selective to language but influence music perception as well.
Long-term effect of high-intensity laser therapy in the treatment of patients with chronic low back pain: a randomized blinded placebo-controlled trial
(2013)

The aim of this study was to compare the effect of high-intensity laser therapy (HILT), alone or combined with exercise, in the treatment of chronic low back pain (CLBP). A total of 72 male patients participated in this study, with a mean (SD) age of 32.81 (4.48) years. Patients were randomly assigned into three groups and treated with HILT plus exercise (HILT + EX), placebo laser plus exercise (PL + EX), and HILT alone in groups 1, 2, and 3, respectively. The outcomes measured were lumbar range of motion (ROM), pain level by visual analog scale (VAS), and functional disability by both the Roland Disability Questionnaire (RDQ) and the Modified Oswestry Disability Questionnaire (MODQ). Statistical analyses were performed to compare the differences between baseline and post-treatment measurements. The level of statistical significance was set as P < 0.05. ROM significantly increased after 4 weeks of treatment in all groups, then significantly decreased after 12 weeks of follow-up, but was still significantly more than the baseline value in groups 1 and 2. VAS, RDQ, and MODQ results showed significant decrease post-treatment in all groups, although the RDQ and MODQ results were not significantly different between groups 2 and 3. HILT combined with exercise appears to be more effective in patients with CLBP than either HLLT alone or placebo laser with exercise.
A Lightweight Multicast Authentication Mechanism for Small Scale IoT Applications
(2013)

Security is very important for Internet of Things (IoTs). As a main communication mode, the security mechanism for multicast is not only the measure to ensure secured communications, but also the precondition for other security services. With the analysis of Nyberg's fast one-way accumulator and its security, we discover that it has the property of absorbency in addition to the one-way and quasi-communicative property that makes it very suitable for applications in which accumulated items are dynamic. In this paper, we revise the original Nyberg's fast one-way accumulator and construct a lightweight multicast authentication mechanism for small scale IoT applications. We analyze the security of the mechanism in detail. In addition, we evaluate seven performance aspects of the mechanism.
Efficient set intersection for inverted indexing
(2010)

Conjunctive Boolean queries are a key component of modern information retrieval systems, especially when Web-scale repositories are being searched. A conjunctive query q is equivalent to a |q|-way intersection over ordered sets of integers, where each set represents the documents containing one of the terms, and each integer in each set is an ordinal document identifier. As is the case with many computing applications, there is tension between the way in which the data is represented, and the ways in which it is to be manipulated. In particular, the sets representing index data for typical document collections are highly compressible, but are processed using random access techniques, meaning that methods for carrying out set intersections must be alert to issues to do with access patterns and data representation. Our purpose in this article is to explore these trade-offs, by investigating intersection techniques that make use of both uncompressed “integer” representations, as well as compressed arrangements. We also propose a simple hybrid method that provides both compact storage, and also faster intersection computations for conjunctive querying than is possible even with uncompressed representations.
Inverted index compression and query processing with optimized document ordering
(2009)

Web search engines use highly optimized compression schemes to decrease inverted index size and improve query throughput, and many index compression techniques have been studied in the literature. One approach taken by several recent studies first performs a renumbering of the document IDs in the collection that groups similar documents together, and then applies standard compression techniques. It is known that this can significantly improve index compression compared to a random document ordering. We study index compression and query processing techniques for such reordered indexes. Previous work has focused on determining the best possible ordering of documents. In contrast, we assume that such an ordering is already given, and focus on how to optimize compression methods and query processing for this case. We perform an extensive study of compression techniques for document IDs and present new optimizations of existing techniques which can achieve significant improvement in both compression and decompression performances. We also propose and evaluate techniques for compressing frequency values for this case. Finally, we study the effect of this approach on query processing performance. Our experiments show very significant improvements in index size and query processing speed on the TREC GOV2 collection of 25.2 million web pages.
Super-Scalar RAM-CPU Cache Compression
(2005)

High-performance data-intensive query processing tasks like OLAP, data mining or scientific data analysis can be severely I/O bound, even when high-end RAID storage systems are used. Compression can alleviate this bottleneck only if encoding and decoding speeds significantly exceed RAID I/O bandwidth. For this purpose, we propose three new versatile compression schemes (PDICT, PFOR, and PFOR-DELTA) that are specifically designed to extract maximum IPC from modern CPUs. We compare these algorithms with compression techniques used in (commercial) database and information retrieval systems. Our experiments on the MonetDB/X100 database system, using both DSM and PAX disk storage, show that these techniques strongly accelerate TPC-H performance to the point that the I/O bottleneck is eliminated.
Inverted files for text search engines
(2006)

The technology underlying text search engines has advanced dramatically in the past decade. The development of a family of new index representations has led to a wide range of innovations in index storage, index construction, and query evaluation. While some of these developments have been consolidated in textbooks, many specific techniques are not widely known or the textbook descriptions are out of date. In this tutorial, we introduce the key techniques in the area, describing both a core implementation and how the core can be enhanced through a range of extensions. We conclude with a comprehensive bibliography of text indexing literature.
Retinal blood vessel segmentation using matched filter and Laplacian of Gaussian
(2016)

Automated blood vessel segmentation of retinal images offers huge potential benefits for medical diagnosis of different ocular diseases. In this paper, 2D Matched Filters (MF) are applied to fundus retinal images to detect vessels which are enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE) method. Due to the Gaussian nature of blood vessel profile, the MF with Gaussian kernel often misclassifies non-vascular structures (e.g., step, ramp or other transients) as vessels. To avoid such false detection, this paper introduces Laplacian of Gaussian (LoG) filters in the vessel segmentation process. The inherent zero-crossing property of LoG filter is used in the algorithm, along with the MF, in order to extract vessels reliably from the retinal images. The proposed method is validated against three publicly available databases, STARE, DRIVE and HRF. Simulation results show that the proposed method is able to segment vessels accurately from the three database images with an average accuracy that is competitive to the existing methodologies.
Deep Learning for the Classification of Lung Nodules
(2016)

Deep learning, as a promising new area of machine learning, has attracted a rapidly increasing attention in the field of medical imaging. Compared to the conventional machine learning methods, deep learning requires no hand-tuned feature extractor, and has shown a superior performance in many visual object recognition applications. In this study, we develop a deep convolutional neural network (CNN) and apply it to thoracic CT images for the classification of lung nodules. We present the CNN architecture and classification accuracy for the original images of lung nodules. In order to understand the features of lung nodules, we further construct new datasets, based on the combination of artificial geometric nodules and some transformations of the original images, as well as a stochastic nodule shape model. It is found that simplistic geometric nodules cannot capture the important features of lung nodules.
Ethical issues raised by the treatment of gender-variant prepubescent children.
(2014)

Transgender issues and transgender rights have become increasingly a matter of media attention and public policy debates. Reflecting changes in psychiatric perspectives, the diagnosis of "trans-sexualism" first appeared in the International Statistical Classification of Diseases and Related Health Problems in 1975 and shortly thereafter, in 1980, in the Diagnostic and Statistical Manual of Mental Disorders. Since that time, international standards of care have been developed, and today those standards are followed by clinicians across diverse cultures. In many instances, treatment of older adolescents and adults is covered by national health care systems and, in some cases, by private health insurance. Most recently, the Medicare ban on coverage for gender reassignment surgery was lifted in 2014. In contrast to the relative lack of controversy about treating adolescents and adults, there is no expert clinical consensus regarding the treatment of prepubescent children who meet diagnostic criteria for what was referred to in both DSM-IV-TR and ICD-10 as gender identity disorder in children and now in DSM-5 as gender dysphoria. One reason for the differing attitudes has to do with the pervasive nature of gender dysphoria in older adolescents and adults: it rarely desists, and so the treatment of choice is gender or sex reassignment. On the subject of treating children, however, as the World Professional Association for Transgender Health notes in their latest Standards of Care, gender dysphoria in childhood does not inevitably continue into adulthood, and only 6 to 23 percent of boys and 12 to 27 percent of girls treated in gender clinics showed persistence of their gender dysphoria into adulthood. Further, most of the boys' gender dysphoria desisted, and in adulthood, they identified as gay rather than as transgender. In an effort to clarify best treatment practices for transgender individuals, a recent American Psychiatric Association Task Force on the Treatment of Gender Identity outlined three differing approaches to treating prepubescent gender dysphoric children.
Revisiting The American Voter on Twitter
(2017)

The American Voter - a seminal work in political science - uncovered the multifaceted nature of voting behavior which has been corroborated in electoral research for decades since. In this paper, we leverage The American Voter as an analysis framework in the realm of computational political science, employing the factors of party, personality, and policy to structure the analysis of public discourse on online social media during the 2016 U.S. presidential primaries. Our analysis of 50 million tweets reveals the continuing importance of these three factors; our understanding is also enriched by the application of sentiment analysis techniques. The overwhelmingly negative sentiment of conversations surrounding 10 major presidential candidates reveals more "crosstalk" from Democratic leaning users towards Republican candidates, and less vice-versa. We uncover the lack of moderation as the most discussed personality dimension during this campaign season, as the political field becomes more extreme - Clinton and Rubio are perceived as moderate, while Trump, Sanders, and Cruz are not. While the most discussed issues are foreign policy and immigration, Republicans tweet more about abortion than Democrats who tweet more about gay rights than Republicans. Finally, we illustrate the importance of multifaceted political discourse analysis by applying regression to quantify the impact of party, personality, and policy on national polls.
Event Correlation: Language and Semantics
(2003)

300.twolf 3000 102 2945 102 2945 Hardware CPU: Intel(R) Xeon(R) CPU 5150 @ 2.66GHz CPU MHz: 2660 FPU: Integrated CPU(s) enabled: 4 cores, 2 chips, 2 cores/chip CPU(s) orderable: 1,2 chip(s) Parallel: No Primary Cache: 32KB(I) + 32KB(D) on chip, per core Secondary Cache: 4096KB(I+D) on chip, per chip, shared L3 Cache: N/A Other Cache: N/A Memory: 4 x 1024MB ECC FB-DIMM DDR2-667MHz Disk Subsystem: 1 x 250GB SATA HDD Other Hardware: Software Operating System: Red Hat Enterprise Linux 4 Update 3 EM64T Compiler: Intel C++ Compiler 9.1.042 for EM64T File System: ext3 System State: Runlevel 3
There is no 16-Clue Sudoku: Solving the Sudoku Minimum Number of Clues Problem
(2014)

We apply our new hitting set enumeration algorithm to solve the sudoku minimum number of clues problem, which is the following question: What is the smallest number of clues (givens) that a sudoku puzzle may have? It was conjectured that the answer is 17. We have performed an exhaustive search for a 16-clue sudoku puzzle, and we did not find one, thereby proving that the answer is indeed 17. This article describes our method and the actual search. The hitting set problem is computationally hard; it is one of Karp’s twenty-one classic NP-complete problems. We have designed a new algorithm that allows us to efficiently enumerate hitting sets of a suitable size. Hitting set problems have applications in many areas of science, such as bioinformatics and software testing. ∗School of Mathematical Sciences, University College Dublin, Ireland. E-mail: gary.mcguire@ucd.ie
Collaborative detection of cyberbullying behavior in Twitter data
(2015)

As the size of Twitter© data is increasing, so are undesirable behaviors of its users. One of such undesirable behavior is cyberbullying, which may even lead to catastrophic consequences. Hence, it is critical to efficiently detect cyberbullying behavior by analyzing tweets, if possible in realtime. Prevalent approaches to identify cyberbullying are mainly stand-alone and thus, are time-consuming. This research improves detection task using the principles of collaborative computing. Different collaborative paradigms are suggested and discussed in this paper. Preliminary results indicate an improvement in time and accuracy of the detection mechanism over the stand-alone paradigm.